Returns the FDA / clinical-trial calendar — upcoming drug approval target dates and the tickers behind each drug.
Window is fixed at today − 14 days to today + 60 days, sorted by date ascending.
Use for upcoming FDA decisions, PDUFA dates, biotech catalysts.
Returns: { data: [{ targetDate, clinicalTrials: [{ drugName, ticker }] }],
count, window }.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
Output schema / (root)Previous value: -{
- "properties": {
- "result": {
- "title": "Result",
- "type": "string"
- }
- },
- "required": [
- "result"
- ],
- "title": "get_clinical_trialsOutput",
- "type": "object"
-}New value: +nullDoes the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false. The description adds valuable behavioral context beyond annotations: a fixed window ('today − 14 days to today + 60 days'), sorting ('by date ascending'), and the exact return structure. While it does not discuss error handling or rate limits, the added details meet the lowered bar set by the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and well-structured: a one-sentence summary, a sentence on the fixed window and sorting, a sentence on intended use, and a formatted return layout. Every sentence earns its place with no fluff or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given its simplicity (no parameters, no output schema), the description is fully self-contained. It communicates the purpose, the exact data window, the ordering, the intended application, and the shape of the returned array. Nothing essential is left unexplained for the agent to invoke the tool successfully.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the schema is trivially 100% covered. Per the rubric, 0 params earns a baseline of 4. The description does not need to explain parameters and instead clarifies the fixed data scope, which is the only relevant input detail.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description begins with a specific verb 'Returns' and names the exact resource: 'FDA / clinical-trial calendar — upcoming drug approval target dates and the tickers behind each drug.' This clearly differentiates from sibling calendar tools like get_earnings_calendar or get_economic_calendar by specifying the domain and content.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit use cases: 'Use for upcoming FDA decisions, PDUFA dates, biotech catalysts.' This gives clear context for when to select this tool, but does not explicitly state when not to use it or name alternative tools, so it falls one point short of full guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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